Prediction of Self-Diffusion in Binary Fluid Mixtures Using Artificial Neural Networks.

Artificial neural networks (ANNs) were developed to accurately predict the self-diffusion constants for individual components in binary fluid mixtures. The ANNs were tested on an experimental database of 4328 self-diffusion constants from 131 mixtures containing 75 unique compounds. The presence of strong hydrogen bonding molecules may lead to clustering or dimerization resulting in non-linear diffusive behavior.

Direct Transfer of Mesoporous Silica Nanoparticles between Macrophages and Cancer Cells.

Macrophages line the walls of microvasculature, extending processes into the blood flow to capture foreign invaders, including nano-scale materials. Using mesoporous silica nanoparticles (MSNs) as a model nano-scale system, we show the interplay between macrophages and MSNs from initial uptake to intercellular trafficking to neighboring cells along microtubules. The nature of cytoplasmic bridges between cells and their role in the cell-to-cell transfer of nano-scale materials is examined, as is the ability of macrophages to function as carriers of nanomaterials to cancer cells.